Exploring the Recognition of Facial Activities Through Around-The-Ear Electrode Arrays (cEEGrids)

Knierim, Michael Thomas; Schemmer, Max; Perusquía-Hernández, Monica

doi:10.1007/978-3-030-88900-5_6

Cited by 2 publications

(5 citation statements)

References 31 publications

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“…In addition, other relatively large amplitude phenomena have been observed with the OpenBCI-cEEGrid combination. Thereby, the distinctive patterns of various facial muscle activities have been found to be captured and differentiated well [4] , [5] .…”

Section: Validation and Applicationmentioning

confidence: 94%

“…Ear-based sensing represents an attractive direction for developers of wearable neurotechnologies or human–computer-interaction modalities due to the multitude of detectable signals from within and around the ears and the inconspicuousness with which the necessary hardware can be positioned (ear-pieces, headphones, glasses, or other headwear) [1] , [2] , [3] . Electrophysiological ear-based sensors have in particular been reported as versatile tools to collect both neural and muscle activity [4] , [5] , [6] , [7] . For neural observation, ear-electroencephalography (ear-EEG) represents a less comprehensive (i.e.…”

Section: Hardware In Contextmentioning

confidence: 99%

“…Thereby, while electrodes are placed in less visible recording sites, for example, heart rate activity [6] , [7] or facial muscle activity can be observed from afar. This can, for instance, be used to detect different facial expressions of emotions [5] , [8] .…”

Section: Hardware In Contextmentioning

confidence: 99%

“…For the moment, large amplitude and frequency-based observations have been documented. Possible use cases that scholars might be interested in are: Multiple hour EEG or EMG recordings in field settings with high comfort and signal quality [7] , [14] Studying typical EEG phenomena related to auditory stimulation (e.g., detection of directed speaker attention) [7] , [15] Sleep stage detection [16] Mental workload observation [17] Detecting facial activities and actions (e.g., as input to control digital applications or to detect facial expressions of emotion) [4] , [5] …”

Section: Hardware In Contextmentioning

confidence: 99%

“…Detecting facial activities and actions (e.g., as input to control digital applications or to detect facial expressions of emotion) [4] , [5] …”

Section: Hardware In Contextmentioning

confidence: 99%

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